Elevating smart city mobility using RAE-LSTM fusion for next-gen traffic prediction
Najat Rafalia,
Idriss Moumen,
Fatima Zahra Raji
et al.
Abstract:The burgeoning demand for efficient urban traffic management necessitates accurate prediction of traffic congestion, spotlighting the essence of time series data analysis. This paper delves into the utilization of sophisticated deep learning methodologies, particularly long short-term memory (LSTM) networks, convolutional neural networks (CNN), and their amalgamations like Conv-LSTM and bidirectional-LSTM (Bi-LSTM), to elevate the precision of traffic pattern forecasting. These techniques showcase promise in e… Show more
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